Software Engineer – Machine Learning Engineer
FactSet
full-time
Posted on:
Location Type: Hybrid
Location: London • 🇬🇧 United Kingdom
Visit company websiteJob Level
Mid-LevelSenior
Tech Stack
AWSCloudDockerEC2KerasMongoDBNeo4jPySparkPythonPyTorchTensorflow
About the role
- Manage and deploy various cloud-based infrastructure
- Develop a roadmap for management and growth of existing pipelines and infrastructure for serving ML and AI solutions
- Deployment and maintenance of models, databases, and applications
- Support work on various AI/ML projects including entity and topic modeling, semantic tagging/enrichment, information extraction, transfer learning, and graph neural networks
- Develop dashboards and visualizations for financial experts
- Ingest and analyse structured and unstructured data
- Develop processes for data collection, quality assessment, and quality control
- Keep up to date with state-of-the-art approaches and technological advancement
- Collaborate with other Engineering teams
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning or a related field
- 3 + years of working experience as a Software Engineer/ Machine Learning Engineer
- Experience with cloud-based infrastructure (AWS preferred)
- Familiarity with ML, NLP and GenAI (including RAG, Prompt Engineering, Vector DBs)
- Successful history of writing production grade code and releasing in an enterprise environment
- Team player
- Fluent in English; ability to communicate about complex subjects to non-technical stakeholders
- Highly proficient in Python
- Experience with OpenAI, Anthropic, and other large language models.
- Prior experience working with unstructured data (text content, JSON records)
- Working with Agile development practices in a production environment
- Experience with AWS environment [SageMaker, S3, Athena, Glue, ECS, EC2]
- Experience with Agentic workflows and MCP
- Experience working with large volumes of data in a stream or batch processing environment.
- Prior experience with Docker and API development.
- Usage of MongoDB
- Familiarity with deep learning libraries (Keras, PyTorch, Tensorflow)
- Familiarity with big data tool chain (e.g. Pyspark, Hive)
- Experience with information extraction, parsing and segmentation
- Knowledge of ontologies, taxonomy resolution and disambiguation.
- Experience in Unsupervised Learning techniques Density Estimation, Clustering and Topic Modelling.
- Graph database experience (AWS Neptune, Neo4j)
Benefits
- Flexible working hours
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonMachine LearningNatural Language ProcessingDeep LearningInformation ExtractionUnsupervised LearningData AnalysisProduction Grade CodeGraph Neural NetworksData Quality Assessment
Soft skills
Team playerCommunicationCollaborationProblem SolvingAdaptability
Certifications
Bachelor’s degree in Computer ScienceMaster’s degree in Machine Learning